Specific residues at every third position of siRNA shape its efficient RNAi activity.

Katoh T, Suzuki T - Nucleic Acids Res. (2007)

Bottom Line:
Different efficacy of each siRNA is considered to result from sequence preference by protein components in RNAi.Since there was an obvious correlation between siRNA activity and its binding affinity to TRBP, a partner protein of human Dicer, the 3-nt periodicity might correlate with the affinity to TRBP.As an algorithm ('siExplorer') developed by this observation successfully calculated the activities of siRNAs targeting endogenous human genes, the 3-nt periodicity provides a new aspect unveiling siRNA functionality.

Affiliation: Department of Chemistry and Biotechnology, Graduate School of Engineering, University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan.

ABSTRACTSmall interfering RNA (siRNA) induces sequence-specific post-transcriptional gene silencing in mammalian cells. Different efficacy of each siRNA is considered to result from sequence preference by protein components in RNAi. To obtain mechanistic insight into siRNA functionality, here we describe a complete data set of siRNA activities targeting all possible position of a single mRNA in human cells. Seven hundred and two siRNAs covering open reading frame of enhanced green fluorescent protein mRNA ( 720 bases) were examined with minimized error factors. The most important finding is that specific residues at every third position of siRNAs greatly influence its RNAi activity; the optimized base composition at positions 3n + 1 (4,7,10,13,16,19) in siRNAs have positive effects on the activity, which can explain the waving siRNA activity with 3 nucleotides (nt) periodicity in the sequential positions of mRNAs. Since there was an obvious correlation between siRNA activity and its binding affinity to TRBP, a partner protein of human Dicer, the 3-nt periodicity might correlate with the affinity to TRBP. As an algorithm ('siExplorer') developed by this observation successfully calculated the activities of siRNAs targeting endogenous human genes, the 3-nt periodicity provides a new aspect unveiling siRNA functionality.

Figure 4: Effect of the base position of 702 siRNAs on their ability to silence EGFP expression. (A) Effect of certain positions in the 702 siRNAs on their activities. The correlation coefficients between the B values obtained from 1-nt at each position of 702 siRNAs and their activities are plotted in red. The correlation coefficients arising from the correlation analysis of the B values obtained from 18-nt of siRNAs in which each specific position is systematically excluded and the activities are shown in bars. Nucleotide positions represent the passenger strand of the siRNA. (B) Effect of different bases at each specific nucleotide position in the 702 siRNAs on their activities. The base-specific values (one or zero) were obtained as described in the text. The correlation coefficients between the base-specific values of 702 siRNAs and their activities are plotted for each base (A in red, U in pink, G in green and C in blue). Bases on the top of the graph that have positive effects on the activities are shown in red (beyond 0.2) or in orange (beyond 0.1) at the top of the graph. Bases on the bottom of the graph with negative effects on the activities are shown in blue (under −0.2) or in light blue (under −0.1) at the bottom of the graph.

Mentions:
We next analyzed the effect of the residues at certain positions in the siRNA on its RNAi activity. At each position (1–19) of the 702 siRNAs, the R values between the best PX values (PA, PU, PG and PC are 0.4, 0.35, 0.15 and 0.1) and their activities (1-nt analysis) are plotted in Figure 4A. For example, if the position 1 of a certain siRNA has A-base, its PX value is 0.4. The result shows that Px values for positions 4, 7, 10, 13, 16 and 19 (i.e. the 3n + 1 positions, where n = counting number) correlate relatively well with the activity to yield positive R factor, while the Px values for positions 2, 5, 8, 11, 14 and 17 (i.e. the 3n + 2 positions) do not correlate with the activity (R factors are nearly 0). Positions 3, 6, 9, 12, 15 and 18 (i.e. the 3n positions) show R factors that fall between the R factors of the 3n + 1 and 3n + 2 positions. In contrast, we also examined the correlation between the B values calculated from 18-nt positions for 702 siRNAs in which each specific position had been systematically excluded and their activity (18-nt analysis) (Figure 4A, bars). The resulting R factors arising from this 18-nt analysis showed a clear negative relationship with the R factors of the 1-nt analysis at each position (Figure 4A, plots). Thus, the R factors of the 18-nt analysis for 702 siRNAs that lack a residue at position 3n + 1 show a significant reduction, the R factors from the data omitting position 3n show a mild reduction, and the R factors from the data omitting position 3n + 2 show no reduction. Position 1 does not strongly correlate with the activity.Figure 4.

Figure 4: Effect of the base position of 702 siRNAs on their ability to silence EGFP expression. (A) Effect of certain positions in the 702 siRNAs on their activities. The correlation coefficients between the B values obtained from 1-nt at each position of 702 siRNAs and their activities are plotted in red. The correlation coefficients arising from the correlation analysis of the B values obtained from 18-nt of siRNAs in which each specific position is systematically excluded and the activities are shown in bars. Nucleotide positions represent the passenger strand of the siRNA. (B) Effect of different bases at each specific nucleotide position in the 702 siRNAs on their activities. The base-specific values (one or zero) were obtained as described in the text. The correlation coefficients between the base-specific values of 702 siRNAs and their activities are plotted for each base (A in red, U in pink, G in green and C in blue). Bases on the top of the graph that have positive effects on the activities are shown in red (beyond 0.2) or in orange (beyond 0.1) at the top of the graph. Bases on the bottom of the graph with negative effects on the activities are shown in blue (under −0.2) or in light blue (under −0.1) at the bottom of the graph.

Mentions:
We next analyzed the effect of the residues at certain positions in the siRNA on its RNAi activity. At each position (1–19) of the 702 siRNAs, the R values between the best PX values (PA, PU, PG and PC are 0.4, 0.35, 0.15 and 0.1) and their activities (1-nt analysis) are plotted in Figure 4A. For example, if the position 1 of a certain siRNA has A-base, its PX value is 0.4. The result shows that Px values for positions 4, 7, 10, 13, 16 and 19 (i.e. the 3n + 1 positions, where n = counting number) correlate relatively well with the activity to yield positive R factor, while the Px values for positions 2, 5, 8, 11, 14 and 17 (i.e. the 3n + 2 positions) do not correlate with the activity (R factors are nearly 0). Positions 3, 6, 9, 12, 15 and 18 (i.e. the 3n positions) show R factors that fall between the R factors of the 3n + 1 and 3n + 2 positions. In contrast, we also examined the correlation between the B values calculated from 18-nt positions for 702 siRNAs in which each specific position had been systematically excluded and their activity (18-nt analysis) (Figure 4A, bars). The resulting R factors arising from this 18-nt analysis showed a clear negative relationship with the R factors of the 1-nt analysis at each position (Figure 4A, plots). Thus, the R factors of the 18-nt analysis for 702 siRNAs that lack a residue at position 3n + 1 show a significant reduction, the R factors from the data omitting position 3n show a mild reduction, and the R factors from the data omitting position 3n + 2 show no reduction. Position 1 does not strongly correlate with the activity.Figure 4.

Bottom Line:
Different efficacy of each siRNA is considered to result from sequence preference by protein components in RNAi.Since there was an obvious correlation between siRNA activity and its binding affinity to TRBP, a partner protein of human Dicer, the 3-nt periodicity might correlate with the affinity to TRBP.As an algorithm ('siExplorer') developed by this observation successfully calculated the activities of siRNAs targeting endogenous human genes, the 3-nt periodicity provides a new aspect unveiling siRNA functionality.

Affiliation:
Department of Chemistry and Biotechnology, Graduate School of Engineering, University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan.

ABSTRACTSmall interfering RNA (siRNA) induces sequence-specific post-transcriptional gene silencing in mammalian cells. Different efficacy of each siRNA is considered to result from sequence preference by protein components in RNAi. To obtain mechanistic insight into siRNA functionality, here we describe a complete data set of siRNA activities targeting all possible position of a single mRNA in human cells. Seven hundred and two siRNAs covering open reading frame of enhanced green fluorescent protein mRNA ( 720 bases) were examined with minimized error factors. The most important finding is that specific residues at every third position of siRNAs greatly influence its RNAi activity; the optimized base composition at positions 3n + 1 (4,7,10,13,16,19) in siRNAs have positive effects on the activity, which can explain the waving siRNA activity with 3 nucleotides (nt) periodicity in the sequential positions of mRNAs. Since there was an obvious correlation between siRNA activity and its binding affinity to TRBP, a partner protein of human Dicer, the 3-nt periodicity might correlate with the affinity to TRBP. As an algorithm ('siExplorer') developed by this observation successfully calculated the activities of siRNAs targeting endogenous human genes, the 3-nt periodicity provides a new aspect unveiling siRNA functionality.